Multi-scale feature fusion extraction algorithm and its application in intelligent mineral prospectivity prediction

<p indent="0mm">Intelligent mineral prospectivity prediction utilizes artificial intelligence algorithms to explore the relationship between multi-source geological prospecting data and mineral deposit locations, in order to delineate potential prospecting areas of mineral resources. Deep learning methods can independently extract relevant features related to the mineralization in depth. It is helpful to discover hidden critical information for prospecting mineral resources. Especially, the convolutional neural network is highly capable for retaining local spatial information of multi-source ore-controlling factors. It plays an important role in searching prospecting targets of mineral resources. This paper discussed the task transformation and main challenges of the convolutional neural networks applied in intelligent mineral prospectivity prediction. It can be transformed into a dichotomy classification task of ore-bearing and ore-barren samples of multi-source ore-controlling factors, for quantifying the mineralization potential by using the ore-bearing probability. However, there is a shortcoming of single extraction of features related to the mineralization, which can not satisfy for the requirement of comprehensive extraction of information of multi-channel variable features. Therefore, this paper introduced a multi-scale feature fusion extraction algorithm to have extracted features related to the mineralization from multiple convolutional scales, and then these multi-scale features were fused to improve the diversity and fertility of the obtained features related to the mineralization. Especially, the difference between the channel fused and pixel fused multi-scale feature extraction algorithms was analyzed, and the comparative experiment was carried out. Finally, the framework for intelligent prediction of mineral prospecting was established with steps of the sample establishment, model construction, model evaluation and model prediction. Based on this framework, a case study of the mineral prospectivity prediction of gold deposits in Fengxian County, Shaanxi Province has been carried out by extracting multi-scale fusion features related to the mineralization from multi-source prospecting information. A predictive model has been constructed and some targeting areas with high potential of mineral prospecting have been outlined. This study can provide technical support for intelligent prospecting of mineral resources.